小贝子编程

spark.ml StringIndexer 在 fit() 上抛出'Unseen label'



我正在准备一个玩具spark.ml例子。 Spark version 1.6.0，运行在Oracle JDK version 1.8.0_65，Pyspark，ipython

笔记本之上。

首先，它与Spark，ML，StringIndexer几乎没有任何关系：处理看不见的标签。在将管道拟合到数据集而不是转换数据集时引发异常。在这里，抑制异常可能不是解决方案，因为恐怕在这种情况下，数据集会变得非常混乱。

我的数据集大约是 800Mb 未压缩的，因此可能很难重现(较小的子集似乎可以回避这个问题(。

数据集如下所示：

+--------------------+-----------+-----+-------+-----+--------------------+
|                 url|         ip|   rs|   lang|label|                 txt|
+--------------------+-----------+-----+-------+-----+--------------------+
|http://3d-detmold...|217.160.215|378.0|     de|  0.0|homwillkommskip c...|
|   http://3davto.ru/| 188.225.16|891.0|     id|  1.0|оформить заказ пе...|
| http://404.szm.com/|  85.248.42| 58.0|     cs|  0.0|kliknite tu alebo...|
|  http://404.xls.hu/| 212.52.166|168.0|     hu|  0.0|honlapkészítés404...|
|http://a--m--a--t...|    66.6.43|462.0|     en|  0.0|back top archiv r...|
|http://a-wrf.ru/c...|  78.108.80|126.0|unknown|  1.0|                    |
|http://a-wrf.ru/s...|  78.108.80|214.0|     ru|  1.0|установк фаркопна...|
+--------------------+-----------+-----+-------+-----+--------------------+

预测的值为 label 。整个管道应用于它：

from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler, StringIndexer, OneHotEncoder, Tokenizer, HashingTF
from pyspark.ml.classification import LogisticRegression
train, test = munge(src_dataframe).randomSplit([70., 30.], seed=12345)
pipe_stages = [
    StringIndexer(inputCol='lang', outputCol='lang_idx'),
    OneHotEncoder(inputCol='lang_idx', outputCol='lang_onehot'),
    Tokenizer(inputCol='ip', outputCol='ip_tokens'),
    HashingTF(numFeatures=2**10, inputCol='ip_tokens', outputCol='ip_vector'),
    Tokenizer(inputCol='txt', outputCol='txt_tokens'),
    HashingTF(numFeatures=2**18, inputCol='txt_tokens', outputCol='txt_vector'),
    VectorAssembler(inputCols=['lang_onehot', 'ip_vector', 'txt_vector'], outputCol='features'),
    LogisticRegression(labelCol='label', featuresCol='features')
]
pipe = Pipeline(stages=pipe_stages)
pipemodel = pipe.fit(train)

这是堆栈跟踪：

Py4JJavaError: An error occurred while calling o10793.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 18 in stage 627.0 failed 1 times, most recent failure: Lost task 18.0 in stage 627.0 (TID 23259, localhost): org.apache.spark.SparkException: Unseen label: pl-PL.
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:157)
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:153)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.evalExpr2$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:51)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:49)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:282)
    at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)
    at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)
    at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1136)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1113)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:271)
    at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:159)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
    at org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Unseen label: pl-PL.
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:157)
    at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:153)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.evalExpr2$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:51)
    at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:49)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:389)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:282)
    at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    ... 1 more

最有趣的一行是： 

org.apache.spark.SparkException: Unseen label: pl-PL.

不知道，pl-PL列中的值lang怎么会在label列中混淆，这是一个float，没有string编辑：一些仓促的合并，由于@zero323而得到纠正

我进一步研究它，发现pl-PL是数据集测试部分的值，而不是训练。所以现在我什至不知道在哪里寻找罪魁祸首：它很容易是randomSplit代码，而不是StringIndexer，谁知道还有什么。

我该如何调查？

>Unseen label是与特定列不对应的通用消息。最有可能的问题出在以下阶段：

StringIndexer(inputCol='lang', outputCol='lang_idx')

pl-PL存在于train("lang")中，而不存在于test("lang")中。

您可以使用带有skip的setHandleInvalid进行更正：

from pyspark.ml.feature import StringIndexer
train = sc.parallelize([(1, "foo"), (2, "bar")]).toDF(["k", "v"])
test = sc.parallelize([(3, "foo"), (4, "foobar")]).toDF(["k", "v"])
indexer = StringIndexer(inputCol="v", outputCol="vi")
indexer.fit(train).transform(test).show()
## Py4JJavaError: An error occurred while calling o112.showString.
## : org.apache.spark.SparkException: Job aborted due to stage failure: 
##   ...
##   org.apache.spark.SparkException: Unseen label: foobar.
indexer.setHandleInvalid("skip").fit(train).transform(test).show()
## +---+---+---+
## |  k|  v| vi|
## +---+---+---+
## |  3|foo|1.0|
## +---+---+---+

或者，在最新版本中，keep

： 
indexer.setHandleInvalid("keep").fit(train).transform(test).show()
## +---+------+---+
## |  k|     v| vi|
## +---+------+---+
## |  3|   foo|0.0|
## |  4|foobar|2.0|
## +---+------+---+
好的，

我想我明白了。至少我得到了这个工作。

缓存数据帧(包括训练/测试部分(可以解决此问题。这就是我在这个JIRA问题中发现的：https://issues.apache.org/jira/browse/SPARK-12590。

所以这不是一个错误，只是randomSample可能会在相同但分区不同的数据集上产生不同的结果。显然，我的一些杂音函数(或Pipeline(涉及重新分区，因此，与其定义不同的训练集重新计算的结果可能会有所不同。

我仍然感兴趣的是可重复性：总是"pl-PL"行混合在数据集的错误部分，即它不是随机重新分区。它是确定性的，只是不一致。我想知道它究竟是如何发生的。

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